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All Journal Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edik Informatika : Penelitian Bidang Komputer Sains dan Pendidikan Informatika Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JOISIE (Journal Of Information Systems And Informatics Engineering) Community Engagement and Emergence Journal (CEEJ) ARSY : Jurnal Aplikasi Riset kepada Masyarakat Journal of Applied Data Sciences Indonesian Journal of Intellectual Publication (IJI Publication) JUTECH : Journal Education and Technology Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat Jurnal Sistem Informasi dan Teknik Informasi Jurnal Teknik Informatika JIM ( Journal International Multidiciplinary) Indonesian Journal of Intellectual Publication (IJI Publication)
Debi Setiawan, Debi
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Social Sciences Other

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Journal : Journal of Applied Data Sciences

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Improved Deep Learning Model for Prediction of Dermatitis in Infants Setiawan, Debi; Noratama Putri, Ramalia; Fitri, Imelda; Nizar Hidayanto, Achmad; Irawan, Yuda; Hohashi, Naohiro
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.542

Abstract

Indonesia's equatorial climate, characterized by summer and rainy seasons, presents environmental conditions that contribute to a high incidence of dermatitis in infants. Dermatitis, an inflammatory skin condition, can lead to significant discomfort in infants, affecting their sleep, growth, and development. Early diagnosis is crucial for effective treatment; however, conventional diagnostic methods in clinics and hospitals—such as physical observation and parental interviews—are often time-consuming, subjective, and may lack precision, creating a need for more efficient diagnostic tools. This study explores the application of deep learning models to enhance the accuracy and speed of dermatitis diagnosis in infants. Four convolutional neural network (CNN) models were evaluated: MobileNet, VGG16, ResNet, and a Custom CNN model specifically designed for this study. Using a dataset of 1,088 skin images collected from three regions in Riau Province, Indonesia, we conducted training and testing to assess each model’s performance in distinguishing between dermatitis-affected and healthy skin. Results show that MobileNet and the Custom CNN outperformed other models, achieving accuracy rates of 97% and 85%, respectively. MobileNet’s high accuracy and efficiency make it a viable option for mobile applications, enabling rapid, on-site diagnosis in resource-limited settings. The Custom CNN model, tailored to the unique features of infant skin, also showed promising results. These findings demonstrate the potential of automated, image-based diagnostic tools for assisting medical professionals in early dermatitis detection, improving patient outcomes. This study contributes a valuable diagnostic solution that leverages deep learning to support healthcare providers, particularly in areas with limited access to specialized medical resources.
A Hybrid YOLO–CNN Model for Automatic Detection and Severity Assessment of Atopic Dermatitis in Infant Images Setiawan, Debi; Putri, Ramalia Noratama; Herlina, Sara; Hidayanto, Achmad Nizar; Irawan, Yuda; Hohashi, Naohiro
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1212

Abstract

Atopic dermatitis is one of the most common skin diseases affecting infants and children worldwide and has a particularly high prevalence in tropical countries. Traditional diagnosis methods, which still rely on physical examinations and laboratory tests often face challenges such as delays, high costs, and limited facilities, thereby necessitating an artificial intelligence–based system that is more efficient and accurate. This study aims to develop a hybrid YOLO–CNN model for the automatic detection and severity classification of atopic dermatitis in infants. The dataset comprises 2,000 infant skin images, including lesions categorized as mild, moderate, and severe, obtained from an online repository and field observations conducted in three villages. The labeling process was performed by a specialist doctor to ensure clinical validity. In the first step, YOLO was used to detect the lesion area in real time by generating a bounding box. This produced a region of interest (ROI), which was subsequently analyzed by a CNN model employing transfer learning in the second step to determine the severity level. Experimental results indicate that YOLO achieved high detection performance, with an mAP@0.5 of 91.2% and an F1-score of 90.2%, while the CNN model attained an average accuracy of 85% and a macro-F1 score of 85% in classification. The visualization of predictions indicates that most lesions were detected with confidence levels ≥0.9, confirming the model’s consistency. These findings highlight the potential of the hybrid YOLO–CNN framework as a supportive system for digital clinical diagnosis, applicable to both mobile applications and teledermatology services, particularly in regions with limited medical personnel. Future research should employ larger, multi-center datasets and integrate explainable AI approaches to promote broader clinical adoption.
Co-Authors Abelia Zhai Astra Achmad Nizar Hidayanto Ahmad Jaelani Amir Sarifudin Amir Syamsuadi Aprizald, Aprizaldi Arisandi, Diki Brimi, Arlianda Dona Auriella Elvitria, Luluk Endang Uliyanti Fikroh Afifi Fitri, Imelda Herlina, Sara Hidayat, Dandi Hohashi, Naohiro Husaini, Rizki Ramadhan Husni Mubarak Indria Yusman YS, Tio Insya Hilla Hesti Ira Puspita Sari Irham , Muhammad Islami, Deri Ismunandar Khairunnisa, Armelia Lestari, Suci Sinta Lestiyani, Nurul Liza Trisnawati Luluk Elvitaria Margi Sidoretno, Wahyu Marwan, Deinike Maryam, Nilam Mhd. Arief Hasan, Mhd. Arief Muhammad Irfan Danial Bin Noor Azlin Nasya Sabilla Neni Widaningsih Ningrum , Puspa Noratama Putri, Ramalia Nur Kholifah NURUL AZIZAH Pradhana, Raka Pratama, M.Charlie Puspita Sari, Ira Rahelza Erpan Rahmat Tisnawan Ramalia Noratama Putri Refgi Halim Reni Suryanita Retno, Putri Dwi Rezi Margozefi Simatupang Rudiansyah Ryanto, Stephen Safira Salsabila SALAMUN Salamun Salamun Shucy Ramawati Fadila Siagian, Dewi Sartika Siagian, Lidya Sugiyono . Toyeb, Muhammad Wijaya, Darno Wita Yulianti, Wita Yuda Irawan